When exactly am I overfitting -- contradicting metrics
Generally, a model is considered to be overfitted when there's a huge gap between training and test/validation performances.
So during the training, you monitor the loss on validation data, and training data, and stop training if validation loss stagnates/increases given the training loss keeps decreasing.
In your scenario, I'm not sure what the total loss metric corresponds to. As I said, you've to measure loss on a held-out data other than training data to detect, and prevent overfitting.